Healing at Implants Placed in an Alveolar Ridge with a Sloped Configuration: An Experimental Study in Dogs
Bibliographic record
Abstract
PURPOSE: To study healing around implants placed in an alveolar ridge with a sloped lingual-buccal configuration. MATERIALS AND METHODS: Six Labrador dogs were used. Buccal bone defects were prepared in the mandible after extraction of premolars. Three months later, two test implants with a sloped marginal design and two control implants were placed in the chronic defect area with a sloped lingual-buccal configuration of each premolar region. The test implants were placed in such a way that the buccal margin of the implant coincided with the buccal bone crest. The lingual margin of the control implants was placed to a similar depth as the lingual margin of the test implants. Abutments were connected to the implants in the right mandibular premolar region and flaps were sutured around the neck of the abutments. In the left side of the mandible, cover screws were placed and the flaps were sutured to cover the implants. Biopsies were obtained 4 months later and prepared for histological examination. RESULTS: It was demonstrated that healing around implants placed in an alveolar ridge with a sloped lingual-buccal configuration resulted in the preservation of a vertical discrepancy between the lingual and buccal marginal bone levels around implants with either a regular cylindrical outline or a modified marginal portion that matched the slope of the alveolar ridge. CONCLUSION: As the marginal buccal portion of the control implants with a regular design had no bone support, it is suggested that implants with a modified marginal portion may be considered in recipient sites with a sloped lingual-buccal configuration.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".